On constraining projections of future climate using observations and simulations from multiple climate models

TL;DR

This paper develops a hierarchical Bayesian framework to incorporate observational data and emergent relationships among climate models, reducing uncertainty in Arctic warming projections by up to 30%.

Contribution

It introduces a novel coexchangeable Bayesian approach that accounts for internal and natural variability, improving the accuracy of climate projections.

Findings

Projected Arctic warming may be over 2°C lower when constrained.

Uncertainty in projections can be reduced by up to 30%.

Ignoring internal variability can bias climate model projections.

Abstract

Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships on projections of future warming in the Arctic at the end of the 21st century. We propose a hierarchical Bayesian framework based on a coexchangeable representation of the relationship between climate models and the Earth system. We show how emergent constraints fit into the coexchangeable representation, and extend it to account for internal variability simulated by the models and natural variability in the Earth system. Our analysis shows that projected warming in some regions of the Arctic may be more than 2C lower and our uncertainty reduced by up to 30% when constrained by historical observations. A detailed theoretical comparison with existing multi-model projection frameworks is also provided. In particular, we show that projections may be biased if we do not account for internal variability in climate model predictions.